{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rk4alsYcy6yL"
      },
      "source": [
        "# RGU-IIT CBR - Results evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knX1w9l9SqMf"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgGINUFOTFVs",
        "outputId": "a6c5c6e2-a99d-4e32-ae1b-5376431d6853"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.2.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.3)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.4 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.20.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.4->datasets) (4.10.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import files\n",
        "from io import BytesIO\n",
        "from heapq import nlargest\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset, Dataset, DatasetDict\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "from nltk.translate.bleu_score import SmoothingFunction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPbp8P7SXyfm"
      },
      "source": [
        "## Load cases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6zH7kThuX1Wl"
      },
      "outputs": [],
      "source": [
        "test_df_mistral = pd.read_csv(\"resources/generated_results_embddings.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41zx9Yc50xy5"
      },
      "source": [
        "## Cosine calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVUXbzkE00Xs",
        "outputId": "13807075-bca0-49a3-bbaf-54b7991c802f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32\n"
          ]
        }
      ],
      "source": [
        "selected_columns_test = [\n",
        "                    'answer_mistral_matching_emb',\n",
        "                    'no_rag_pipeline_result_mistral_matching_emb',\n",
        "                    'normal_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
        "                    'normal_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
        "                    'legal_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
        "                    'legal_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
        "                    'angle_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
        "                    'angle_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
        "                    'normal_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
        "                    'normal_bert_hybrid_case_k3_result_mistral_matching_emb',\n",
        "                    'legal_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
        "                    'legal_bert_hybrid_case_k3_result_mistral_matching_emb',\n",
        "                    'angle_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
        "                    'angle_bert_hybrid_case_k3_result_mistral_matching_emb'\n",
        "                    ]\n",
        "\n",
        "test_df_mistral = test_df_mistral.loc[:, selected_columns_test]\n",
        "test_df_mistral.drop(index=[3, 33, 26], inplace=True)\n",
        "test_df_mistral.reset_index(drop=True, inplace=True)\n",
        "\n",
        "print(len(test_df_mistral))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6wKGGmS5AwZ",
        "outputId": "aa3142bf-b629-4627-eea7-3e24cbf405c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['answer_mistral_matching_emb',\n",
            "       'no_rag_pipeline_result_mistral_matching_emb',\n",
            "       'normal_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
            "       'normal_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
            "       'legal_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
            "       'legal_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
            "       'angle_bert_hybrid_snippet_k1_result_mistral_matching_emb',\n",
            "       'angle_bert_hybrid_case_k1_result_mistral_matching_emb',\n",
            "       'normal_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
            "       'normal_bert_hybrid_case_k3_result_mistral_matching_emb',\n",
            "       'legal_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
            "       'legal_bert_hybrid_case_k3_result_mistral_matching_emb',\n",
            "       'angle_bert_hybrid_snippet_k3_result_mistral_matching_emb',\n",
            "       'angle_bert_hybrid_case_k3_result_mistral_matching_emb'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(test_df_mistral.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nl6wcY3u080h"
      },
      "outputs": [],
      "source": [
        "def rename_columns(df):\n",
        "    df.rename(columns={\n",
        "        'answer_mistral_matching_emb': 'answer',\n",
        "        'no_rag_pipeline_result_mistral_matching_emb': 'no_rag',\n",
        "        'normal_bert_hybrid_snippet_k1_result_mistral_matching_emb': 'k1_nbert_hybrid_snippet',\n",
        "        'normal_bert_hybrid_case_k1_result_mistral_matching_emb': 'k1_nbert_hybrid_case',\n",
        "        'legal_bert_hybrid_snippet_k1_result_mistral_matching_emb': 'k1_lbert_hybrid_snippet',\n",
        "        'legal_bert_hybrid_case_k1_result_mistral_matching_emb': 'k1_lbert_hybrid_case',\n",
        "        'angle_bert_hybrid_snippet_k1_result_mistral_matching_emb': 'k1_abert_hybrid_snippet',\n",
        "        'angle_bert_hybrid_case_k1_result_mistral_matching_emb': 'k1_abert_hybrid_case',\n",
        "        'normal_bert_hybrid_snippet_k3_result_mistral_matching_emb': 'k3_nbert_hybrid_snippet',\n",
        "        'normal_bert_hybrid_case_k3_result_mistral_matching_emb': 'k3_nbert_hybrid_case',\n",
        "        'legal_bert_hybrid_snippet_k3_result_mistral_matching_emb': 'k3_lbert_hybrid_snippet',\n",
        "        'legal_bert_hybrid_case_k3_result_mistral_matching_emb': 'k3_lbert_hybrid_case',\n",
        "        'angle_bert_hybrid_snippet_k3_result_mistral_matching_emb': 'k3_abert_hybrid_snippet',\n",
        "        'angle_bert_hybrid_case_k3_result_mistral_matching_emb': 'k3_abert_hybrid_case',\n",
        "    }, inplace=True)\n",
        "\n",
        "rename_columns(test_df_mistral)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "3cXSRgjbzHGz",
        "outputId": "ea6dc3a6-62b4-45a7-b30f-333e4240c90a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "{'no_rag': 0.8966508650284504, 'k1_nbert_hybrid_snippet': 0.898617541252277, 'k1_nbert_hybrid_case': 0.9067778640759261, 'k1_lbert_hybrid_snippet': 0.902049012888654, 'k1_lbert_hybrid_case': 0.9042713402364871, 'k1_abert_hybrid_snippet': 0.9121339993082246, 'k1_abert_hybrid_case': 0.9074388020993048, 'k3_nbert_hybrid_snippet': 0.9006676002961549, 'k3_nbert_hybrid_case': 0.8998408881838149, 'k3_lbert_hybrid_snippet': 0.9034168072887466, 'k3_lbert_hybrid_case': 0.9045143425594679, 'k3_abert_hybrid_snippet': 0.909197792311198, 'k3_abert_hybrid_case': 0.9141484116287264}\n"
          ]
        }
      ],
      "source": [
        "def string_to_array(embedding_string):\n",
        "    if isinstance(embedding_string, str):\n",
        "        return np.array(embedding_string.strip('[]').split(',')).astype(float)\n",
        "    return embedding_string\n",
        "\n",
        "def calculate_plot_cosine(df, title):\n",
        "    target_columns = ['answer']\n",
        "    cosine_results = {}\n",
        "    for target_col in target_columns:\n",
        "        actual_cols = [col for col in df.columns if target_col != col]\n",
        "        for actual_col in actual_cols:\n",
        "            if df[target_col].notnull().all() and df[actual_col].notnull().all():\n",
        "                target_embeddings = df[target_col].apply(string_to_array).tolist()\n",
        "                actual_embeddings = df[actual_col].apply(string_to_array).tolist()\n",
        "                similarities = []\n",
        "                for index, embedding in enumerate(target_embeddings):\n",
        "                    similarity = cosine_similarity(np.array([target_embeddings[index]]), np.array([actual_embeddings[index]]))\n",
        "                    similarities.append(similarity[0][0])\n",
        "                cosine_results[actual_col] = np.array(similarities)\n",
        "\n",
        "    # Calculate average values and standard deviations\n",
        "    avg_cosine = {}\n",
        "    std_dev_cosine = {}\n",
        "    for col, sims in cosine_results.items():\n",
        "        avg_cosine[col] = np.mean(sims)\n",
        "        std_dev_cosine[col] = np.std(sims)\n",
        "\n",
        "    # Plotting\n",
        "    columns = list(cosine_results.keys())\n",
        "    avg_values = list(avg_cosine.values())\n",
        "    std_dev_values = list(std_dev_cosine.values())\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.errorbar(range(len(columns)), avg_values, yerr=std_dev_values, fmt='o', capsize=5)\n",
        "    plt.xticks(range(len(columns)), columns, rotation=90)\n",
        "    plt.title('Average Cosine Similarities ' + title)\n",
        "    plt.xlabel('Pipeline')\n",
        "    plt.ylabel('Cosine Similarity')\n",
        "\n",
        "    # Annotate each data point\n",
        "    for i, (x, y) in enumerate(zip(range(len(columns)), avg_values)):\n",
        "        plt.text(x, y, f'{avg_values[i]:.4f}', ha='left', va='bottom')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    print()\n",
        "    print()\n",
        "    print(avg_cosine)\n",
        "\n",
        "calculate_plot_cosine(test_df_mistral, 'Mistral-7B')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0J1tggKD05bY"
      },
      "source": [
        "## Corpus BLEU calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxQN4_BI09fj",
        "outputId": "211cbe70-eedf-4ef8-c74b-bfadecab9311"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32\n"
          ]
        }
      ],
      "source": [
        "test_df_mistral = pd.read_csv(\"test_pipeline_results_mistral_mistral_emb.csv\")\n",
        "\n",
        "selected_columns_test = [\n",
        "                    'answer',\n",
        "                    'no_rag_pipeline_result',\n",
        "                    'normal_bert_hybrid_snippet_k1_result',\n",
        "                    'normal_bert_hybrid_case_k1_result',\n",
        "                    'legal_bert_hybrid_snippet_k1_result',\n",
        "                    'legal_bert_hybrid_case_k1_result',\n",
        "                    'angle_bert_hybrid_snippet_k1_result',\n",
        "                    'angle_bert_hybrid_case_k1_result',\n",
        "                    'normal_bert_hybrid_snippet_k3_result',\n",
        "                    'normal_bert_hybrid_case_k3_result',\n",
        "                    'legal_bert_hybrid_snippet_k3_result',\n",
        "                    'legal_bert_hybrid_case_k3_result',\n",
        "                    'angle_bert_hybrid_snippet_k3_result',\n",
        "                    'angle_bert_hybrid_case_k3_result'\n",
        "                    ]\n",
        "\n",
        "test_df_mistral = test_df_mistral.loc[:, selected_columns_test]\n",
        "test_df_mistral.drop(index=[3, 33, 26], inplace=True)\n",
        "test_df_mistral.reset_index(drop=True, inplace=True)\n",
        "\n",
        "print(len(test_df_mistral))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "JjNFySgK0_hw",
        "outputId": "35d76361-a1fb-4d5a-a26b-2fa057c04760"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'no_rag_pipeline_result': 3.9086475473885306e-05, 'normal_bert_hybrid_snippet_k1_result': 5.056270254679212e-05, 'normal_bert_hybrid_case_k1_result': 0.00015506064750220896, 'legal_bert_hybrid_snippet_k1_result': 6.568853183363033e-05, 'legal_bert_hybrid_case_k1_result': 0.00017538000501625547, 'angle_bert_hybrid_snippet_k1_result': 7.522524550933497e-05, 'angle_bert_hybrid_case_k1_result': 0.0003880504498145046, 'normal_bert_hybrid_snippet_k3_result': 5.03209068184337e-05, 'normal_bert_hybrid_case_k3_result': 0.0001241600979801674, 'legal_bert_hybrid_snippet_k3_result': 5.471451632659436e-05, 'legal_bert_hybrid_case_k3_result': 0.0002111563390091318, 'angle_bert_hybrid_snippet_k3_result': 5.68205610920091e-05, 'angle_bert_hybrid_case_k3_result': 0.0001887273428432955}\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "{'no_rag_pipeline_result': 3.9086475473885306e-05, 'normal_bert_hybrid_snippet_k1_result': 5.056270254679212e-05, 'normal_bert_hybrid_case_k1_result': 0.00015506064750220896, 'legal_bert_hybrid_snippet_k1_result': 6.568853183363033e-05, 'legal_bert_hybrid_case_k1_result': 0.00017538000501625547, 'angle_bert_hybrid_snippet_k1_result': 7.522524550933497e-05, 'angle_bert_hybrid_case_k1_result': 0.0003880504498145046, 'normal_bert_hybrid_snippet_k3_result': 5.03209068184337e-05, 'normal_bert_hybrid_case_k3_result': 0.0001241600979801674, 'legal_bert_hybrid_snippet_k3_result': 5.471451632659436e-05, 'legal_bert_hybrid_case_k3_result': 0.0002111563390091318, 'angle_bert_hybrid_snippet_k3_result': 5.68205610920091e-05, 'angle_bert_hybrid_case_k3_result': 0.0001887273428432955}\n"
          ]
        }
      ],
      "source": [
        "bleu_scores = {}\n",
        "raw_scores = []\n",
        "\n",
        "def calculate_bleu(reference, candidate, ngram_range=(1, 1)):\n",
        "    smoothing_function = SmoothingFunction().method2\n",
        "    return corpus_bleu([reference.split()], [candidate.split()], weights=ngram_range, smoothing_function=smoothing_function)\n",
        "\n",
        "def calculate_plot_bleu(df, title):\n",
        "    for index, row in df.iterrows():\n",
        "        reference = row['answer']\n",
        "        row_scores = []\n",
        "        for col in df.columns:\n",
        "            if col != 'answer' and pd.notnull(row[col]):\n",
        "                candidate = row[col]\n",
        "                bleu = calculate_bleu(reference, candidate)\n",
        "                bleu_scores.setdefault(col, []).append(bleu)\n",
        "                row_scores.append(bleu)\n",
        "        raw_scores.append(row_scores)\n",
        "\n",
        "    # Calculating average scores\n",
        "    average_bleu_scores = {col: sum(scores) / len(scores) for col, scores in bleu_scores.items()}\n",
        "    print(average_bleu_scores)\n",
        "\n",
        "    # Plotting average BLEU scores\n",
        "    columns = list(average_bleu_scores.keys())\n",
        "    avg_values_bleu = list(average_bleu_scores.values())\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(range(len(columns)), avg_values_bleu, marker='o', linestyle='-', color='blue')\n",
        "    plt.xticks(range(len(columns)), columns, rotation=90)\n",
        "    plt.title('Average BLEU Scores ' + title)\n",
        "    plt.xlabel('Pipeline')\n",
        "    plt.ylabel('BLEU Score')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    print()\n",
        "    print()\n",
        "    print(average_bleu_scores)\n",
        "\n",
        "calculate_plot_bleu(test_df_mistral, 'Mistral-7B')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
